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这本书涵盖了用R总结数据的基本探索性技术。这些技术通常在正式建模开始之前应用，可以帮助开发更复杂的统计模型。探索技术对于消除或强化关于世界的潜在假设也很重要，这些假设可以通过你所拥有的数据来解决。我们将详细介绍R中的绘图系统以及构造信息数据图形的一些基本原则。我们还将介绍一些用于可视化高维数据的常见多元统计技术。
这本书教你使用R来有效地可视化和探索复杂的数据集。探索性数据分析是数据科学过程的一个关键部分，因为它允许您尖锐地提出问题并改进建模策略。这本书是基于行业领先的约翰霍普金斯数据科学专业，最广泛订阅的数据科学培训项目创建。
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【百度提取码: hxm4】Exploratory Data Analysis with R
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Feature selection techniques are essential for high-dimensional data analysis. In the last two decades, their popularity has been fuelled by the increasing availability of high-throughput biomolecular data where high-dimensionality is a common data property. Recent advances in biotechnologies enable global profiling of various molecular and cellular features at single-cell resolution, resulting in large-scale datasets with increased complexity. These technological developments have led to a resurgence in feature selection research and application in the single-cell field. Here, we revisit feature selection techniques and summarise recent developments. We review their versatile application to a range of single-cell data types including those generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions on which feature selection could have a significant impact. Finally, we consider the scalability and make general recommendations on the utility of each type of feature selection method. We hope this review serves as a reference point to stimulate future research and application of feature selection in the single-cell era.
